Himel Mondal, Rajesh De, Shaikat Mondal, Ayesha Juhi
{"title":"解决初级医疗保健问题的大型语言模型：对远程医疗和医学教育的潜在影响。","authors":"Himel Mondal, Rajesh De, Shaikat Mondal, Ayesha Juhi","doi":"10.4103/jehp.jehp_688_23","DOIUrl":null,"url":null,"abstract":"<p><strong>Background and aim: </strong>Access to quality health care is essential, particularly in remote areas where the availability of healthcare professionals may be limited. The advancement of artificial intelligence (AI) and natural language processing (NLP) has led to the development of large language models (LLMs) that exhibit capabilities in understanding and generating human-like text. This study aimed to evaluate the performance of a LLM, ChatGPT, in addressing primary healthcare issues.</p><p><strong>Materials and methods: </strong>This study was conducted in May 2023 with ChatGPT May 12 version. A total of 30 multiple-choice questions (MCQs) related to primary health care were selected to test the proficiency of ChatGPT. These MCQs covered various topics commonly encountered in primary healthcare practice. ChatGPT answered the questions in two segments-one is choosing the single best answer of MCQ and another is supporting text for the answer. The answers to MCQs were compared with the predefined answer keys. The justifications of the answers were checked by two primary healthcare professionals on a 5-point Likert-type scale. The data were presented as number and percentage.</p><p><strong>Results: </strong>Among the 30 questions, ChatGPT provided correct responses for 28 yielding an accuracy of 93.33%. The mean score for explanation in supporting the answer was 4.58 ± 0.85. There was an inter-item correlation of 0.896, and the average measure intraclass correlation coefficient (ICC) was 0.94 (95% confidence interval 0.88-0.97) indicating a high level of interobserver agreement.</p><p><strong>Conclusion: </strong>LLMs, such as ChatGPT, show promising potential in addressing primary healthcare issues. The high accuracy rate achieved by ChatGPT in answering primary healthcare-related MCQs underscores the value of these models as resources for patients and healthcare providers in remote healthcare settings. This can also help in self-directed learning by medical students.</p>","PeriodicalId":15581,"journal":{"name":"Journal of Education and Health Promotion","volume":"13 ","pages":"362"},"PeriodicalIF":1.4000,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11639534/pdf/","citationCount":"0","resultStr":"{\"title\":\"A large language model in solving primary healthcare issues: A potential implication for remote healthcare and medical education.\",\"authors\":\"Himel Mondal, Rajesh De, Shaikat Mondal, Ayesha Juhi\",\"doi\":\"10.4103/jehp.jehp_688_23\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background and aim: </strong>Access to quality health care is essential, particularly in remote areas where the availability of healthcare professionals may be limited. The advancement of artificial intelligence (AI) and natural language processing (NLP) has led to the development of large language models (LLMs) that exhibit capabilities in understanding and generating human-like text. This study aimed to evaluate the performance of a LLM, ChatGPT, in addressing primary healthcare issues.</p><p><strong>Materials and methods: </strong>This study was conducted in May 2023 with ChatGPT May 12 version. A total of 30 multiple-choice questions (MCQs) related to primary health care were selected to test the proficiency of ChatGPT. These MCQs covered various topics commonly encountered in primary healthcare practice. ChatGPT answered the questions in two segments-one is choosing the single best answer of MCQ and another is supporting text for the answer. The answers to MCQs were compared with the predefined answer keys. The justifications of the answers were checked by two primary healthcare professionals on a 5-point Likert-type scale. The data were presented as number and percentage.</p><p><strong>Results: </strong>Among the 30 questions, ChatGPT provided correct responses for 28 yielding an accuracy of 93.33%. The mean score for explanation in supporting the answer was 4.58 ± 0.85. There was an inter-item correlation of 0.896, and the average measure intraclass correlation coefficient (ICC) was 0.94 (95% confidence interval 0.88-0.97) indicating a high level of interobserver agreement.</p><p><strong>Conclusion: </strong>LLMs, such as ChatGPT, show promising potential in addressing primary healthcare issues. The high accuracy rate achieved by ChatGPT in answering primary healthcare-related MCQs underscores the value of these models as resources for patients and healthcare providers in remote healthcare settings. This can also help in self-directed learning by medical students.</p>\",\"PeriodicalId\":15581,\"journal\":{\"name\":\"Journal of Education and Health Promotion\",\"volume\":\"13 \",\"pages\":\"362\"},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2024-09-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11639534/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Education and Health Promotion\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4103/jehp.jehp_688_23\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q3\",\"JCRName\":\"EDUCATION, SCIENTIFIC DISCIPLINES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Education and Health Promotion","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4103/jehp.jehp_688_23","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"EDUCATION, SCIENTIFIC DISCIPLINES","Score":null,"Total":0}
A large language model in solving primary healthcare issues: A potential implication for remote healthcare and medical education.
Background and aim: Access to quality health care is essential, particularly in remote areas where the availability of healthcare professionals may be limited. The advancement of artificial intelligence (AI) and natural language processing (NLP) has led to the development of large language models (LLMs) that exhibit capabilities in understanding and generating human-like text. This study aimed to evaluate the performance of a LLM, ChatGPT, in addressing primary healthcare issues.
Materials and methods: This study was conducted in May 2023 with ChatGPT May 12 version. A total of 30 multiple-choice questions (MCQs) related to primary health care were selected to test the proficiency of ChatGPT. These MCQs covered various topics commonly encountered in primary healthcare practice. ChatGPT answered the questions in two segments-one is choosing the single best answer of MCQ and another is supporting text for the answer. The answers to MCQs were compared with the predefined answer keys. The justifications of the answers were checked by two primary healthcare professionals on a 5-point Likert-type scale. The data were presented as number and percentage.
Results: Among the 30 questions, ChatGPT provided correct responses for 28 yielding an accuracy of 93.33%. The mean score for explanation in supporting the answer was 4.58 ± 0.85. There was an inter-item correlation of 0.896, and the average measure intraclass correlation coefficient (ICC) was 0.94 (95% confidence interval 0.88-0.97) indicating a high level of interobserver agreement.
Conclusion: LLMs, such as ChatGPT, show promising potential in addressing primary healthcare issues. The high accuracy rate achieved by ChatGPT in answering primary healthcare-related MCQs underscores the value of these models as resources for patients and healthcare providers in remote healthcare settings. This can also help in self-directed learning by medical students.
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